无线Mesh网络中端到端立体视频质量保障的关键技术研究
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摘要
无线Mesh网络可以支持泛在的宽带通信业务，具有覆盖范围大、组网灵活、部署成本低等特点，已成为下一代无线通信系统的重要的组网方式。但其无线信道带宽时变、路径多跳以及节点可移动等特征，都可能使得传输链路不稳定，从而影响对上层业务服务的质量。立体视频蕴含了景物的深度信息，能够与三维的自然场景描述形式一致，因此，能够为人们提供更加逼真的视觉体验，但其在网络（如Mesh网）传输中需克服数据量大、实时性要求强等不利因素，才能获得理想的观看质量。因此，如何保证网络传输中端到端的立体视频流观赏质量成为一项亟待解决的问题，其研究具有重要的理论意义和实际应用价值。
本文以无线Mesh网络为背景，结合立体视频的特点，从传输网络和终端系统两个角度出发，在系统的不同层对端到端的视频质量保障的若干关键技术进行研究。在传输网络中，利用信道丢包模型、Markov模型和M/M/1/K排队模型等数学工具，建立视频失真模型，在媒体接入控制（MAC，MediumAccess Control）层合理选择传输速率，在路由层合理确定传输路径；在终端系统中，解码端利用立体视频的视差相关性，对丢失的数据在应用层进行差错掩盖。
本文的主要工作和创新之处如下：
（1）针对共享单信道网络中存在的“坏鱼问题”（Bad Fish Problem），提出了一种适合视频业务的MAC层速率选择机制。在发送端，首先MAC层利用跨层技术获取应用层的视频帧频要求和容忍丢包率；然后，根据帧频要求，确定每个视频数据包的最大允许时延，并根据这个最大允许时延确定视频MAC帧的最大重传次数；最后，结合接收机当前的信噪比和最大重传次数，在满足应用层丢包率的基础上，为每个视频数据包选择尽可能高的传输速率。在解码端，对于丢失块，则采用误码掩盖的方法来处理。实验结果表明，相比于改变竞争窗口的算法，此方法在视频节点信噪比(SNR，Signal to Noise Ratio)下降需要改变传输速率时，能够减少视频节点的时延和抖动，保证重建视频播放的平滑性，并较少地降低传输节点的吞吐量，从而有效地缓解网络性能异常。
（2）针对源节点和目的节点之间由于多跳传输存在多条路径的问题，提出了一种以视频传输失真最小为判据的路由选择算法。该算法根据失真与网络丢包、延迟之间的函数关系，预测路径的传输失真，并选择失真最小的作为传输路由。该算法较好地考虑了多跳网络的信道误码、路径干扰、网络拥塞等因素对视频传输的影响。首先利用随机均匀模型和Gilbert-Elliott（GE）模型分别模拟信道的随机和突发丢包，然后利用Markov模型计算MAC层的数据包碰撞概率和处理时间，并将每个无线节点建模为M/M/1/K排队系统来预测网络拥塞导致的延迟和丢包，最后得出整条路径的失真并选出网络中的最小失真路由作为传输路径。实验结果表明，与动态源路由（DSR，Dynamic Source Routing）算法相比，该方法能够获得较好的重建视频的主观和客观质量。
（3）在终端的视频应用层，针对随机丢包差错，以立体图像为实验对象，利用像素点之间的视差相关性，提出了一种基于局部可信视差的误码掩盖算法。该算法选择丢失块周围一圈像素作为处理对象，对它们进行视差估计。首先，为了得到较准确的视差，设计了基准点偏置的匹配窗口，并根据需估计的像素点位于丢失块四周的不同方位，而采用不同的偏置中心窗口进行自适应权重的视差估计。然后根据视差左右一致性约束规则剔除不可信点，接着又采用“胜者为王”（WTA，Winner-Takes-All）准则进一步消除误估计视差，这种双消除策略使得估计出的丢失块视差具有较高的精度。最后根据估计的视差进行误码掩盖。实验结果表明，与其他算法相比，该算法能够在计算复杂度相当的情况下，能够显著地提高峰值信噪比（PSNR，Peak Signal to Noise Ratio），并取得较为优质的画面质量。
（4）在终端的视频应用层，利用块的视差相关性，提出了一种随机和连续丢包都适用的基于边界平滑性的立体图像误码掩盖算法。该算法选择丢失块四周相邻的上、下、左、右的区域作为处理区域，为这些区域采用块匹配策略估计视差。然后根据块间视差具有较高的相关性，利用上述得到的四个视差分别来掩盖丢失块，并计算掩盖后的边界平滑性，选择边界平滑性最好的结果作为初步掩盖结果。如果平滑性最好的结果仍大于阈值，则需要对丢失块进行内容自适应判断以得出其属于纹理块、方向块还是遮挡块，并根据判断的类型采用不同的算法修正初步掩盖结果。实验结果表明，相比于其他算法，该算法能够取得较高的PSNR和较好的主观效果，同时其复杂度又相对较小。
Wireless mesh networks (WMNs) are suitable for ubiquitous wide-band communicationservices because of the wide-area coverage, flexibility and low cost. It has become an importantnetworking mode of the next generation wireless communication system. However, itscharacteristics such as time-varying channel, multi-hop and node mobility will make the linksunstable, and consequently degrade the quality of service (QoS) for upper layers. Stereoscopicvideo which contains the depth information of the objects is suitable for3D natural scenes.Therefore it can provide more vivid visual experience for people. Nonetheless, we are facing thechallenges of dealing with large amounts of data and real-time transmission requirements inorder to preserve the stereoscopic video quality over mesh networks. This leads to the problem ofhow to guarantee the end-to-end video quality, and the research of this topic has importanttheoretical significance and practical application value.
This dissertation mainly focuses on key technologies of end-to-end QoS for stereoscopicvideo delivery over WMNs. Taking the characteristics of stereoscopic video into consideration,several methods are proposed at different layers from the perspective of transmission networkand terminal system. We set up the model of video distortion using the channel packet lossmodel, Markov model and M/M/1/K queue model in transmission network. We select thereasonable transmission rate at Medium Access Control (MAC) layer and select the proper routeat routing layer. At the application layer of the terminal system, the decoder implements the errorconcealment for corrosion images, making use of the correlation of disparity.
The contributions of the dissertation are summarized as following:
(1) In order to solve the bad fish problem in a shared single channel network, we propose arate selection mechanism at MAC layer which is suitable for video transmission. First, using thecross-layer technology, MAC layer gets the requirement of frame rate and the tolerance of packetloss rate for video from application layer. Second, the maximum allowed delay of each videopacket is estimated based on the frame rate requirement, which will be used to compute themaximum number of retransmissions for a video MAC frame. Finally, within the margin of thetolerant packet loss rate of application layer, we can adaptively select the highest possibletransmission rate for video MAC frames in terms of current channel quality and the maximumnumber of retransmissions. The lost packet will be dealt with using the algorithm of errorconcealment in the decoder. Experimental results show that, compared with the algorithm ofchanging contention window, the proposed method can reduce the delay and jitter of video service when the transmission rate should change during the Signal to Noise Ratio (SNR) ofvideo node declines, and make the throughputs of the transmission hosts degrade gracefully.Therefore, it increases the quality of reconstructed video to a certain extent and eliminates theperformance anomaly of network effectively.
(2) Aiming at the problem of several routes between the source node and the destinationnode, we use video distortion as the metric to predict the transmission distortion of a route, andselect the route with minimal distortion as the transmission route in terms of the functionalrelationship between the distortion and the network packet loss and delay. The proposedalgorithm considers the factors of channel error, route interference and network congestion. First,random uniform model and Gilbert-Elliott (GE) model are used to simulate the random and burstpacket loss of the channel. Second, Markov model is used to calculate the packet collisionprobability of MAC layer and the processing time, and M/M/1/K queue system is used to predictthe delay and the probability of packet loss. Finally, we calculate the distortion of the total routeand select the route with minimum distortion of the network. Experimental results show thatcompared with the Dynamic Source Routing (DSR) protocol, our algorithm can provide betterobjective and subjective quality of video.
(3) In order to eliminate the random errors for stereoscopic images in video applicationlayer of terminal system, we use the disparity correlation between pixels to propose an errorconcealment algorithm based on local reliable disparities. This algorithm uses the pixelssurrounding the lost block. First, in order to get accurate disparities, we design abase-point-biased window, and use different windows in terms of the position of estimated pixelto perform the adaptive-weight disparity matching. Second, reliable disparities of local area arefigured out according to the principles of disparity constancy of left-right consistency. And thenthe winner-takes-all strategy is adopted to remove the error disparities. These two strategiesmake the estimated disparity of the lost block better. Finally we use the estimated disparity torecover the lost block. Experimental results show that compared with other algorithms, theproposed method can significantly improve the Peak Signal to Noise Ratio (PSNR) value as wellas subjective quality with almost the same computational complexity.
(4) In order to solve the random and consecutive errors for stereoscopic images, we use thedisparity correlation of blocks to propose an error concealment algorithm based on thesmoothness of boundary. First, we select the up, down, left and right blocks around the lost block,and conduct the disparity matching for them. Second, we use these four disparities to conceal thelost block, and calculate the smoothness of the boundary respectively. Then we select the bestsmoothness as the probable concealment result. If the best smoothness is still beyond the threshold, we carry out the content adaptive judgment for the lost block. The lost block will beclassified into one of the three types which are the texture block, directional block, and occlusionblock. We use different algorithms to conceal the lost block according to its type. Experimentalresults show that the proposed method substantially outperforms other algorithms both visuallyand in terms of PSNR, and at the same time, it has a lower computational complexity.
本文以无线Mesh网络为背景，结合立体视频的特点，从传输网络和终端系统两个角度出发，在系统的不同层对端到端的视频质量保障的若干关键技术进行研究。在传输网络中，利用信道丢包模型、Markov模型和M/M/1/K排队模型等数学工具，建立视频失真模型，在媒体接入控制（MAC，MediumAccess Control）层合理选择传输速率，在路由层合理确定传输路径；在终端系统中，解码端利用立体视频的视差相关性，对丢失的数据在应用层进行差错掩盖。
本文的主要工作和创新之处如下：
（1）针对共享单信道网络中存在的“坏鱼问题”（Bad Fish Problem），提出了一种适合视频业务的MAC层速率选择机制。在发送端，首先MAC层利用跨层技术获取应用层的视频帧频要求和容忍丢包率；然后，根据帧频要求，确定每个视频数据包的最大允许时延，并根据这个最大允许时延确定视频MAC帧的最大重传次数；最后，结合接收机当前的信噪比和最大重传次数，在满足应用层丢包率的基础上，为每个视频数据包选择尽可能高的传输速率。在解码端，对于丢失块，则采用误码掩盖的方法来处理。实验结果表明，相比于改变竞争窗口的算法，此方法在视频节点信噪比(SNR，Signal to Noise Ratio)下降需要改变传输速率时，能够减少视频节点的时延和抖动，保证重建视频播放的平滑性，并较少地降低传输节点的吞吐量，从而有效地缓解网络性能异常。
（2）针对源节点和目的节点之间由于多跳传输存在多条路径的问题，提出了一种以视频传输失真最小为判据的路由选择算法。该算法根据失真与网络丢包、延迟之间的函数关系，预测路径的传输失真，并选择失真最小的作为传输路由。该算法较好地考虑了多跳网络的信道误码、路径干扰、网络拥塞等因素对视频传输的影响。首先利用随机均匀模型和Gilbert-Elliott（GE）模型分别模拟信道的随机和突发丢包，然后利用Markov模型计算MAC层的数据包碰撞概率和处理时间，并将每个无线节点建模为M/M/1/K排队系统来预测网络拥塞导致的延迟和丢包，最后得出整条路径的失真并选出网络中的最小失真路由作为传输路径。实验结果表明，与动态源路由（DSR，Dynamic Source Routing）算法相比，该方法能够获得较好的重建视频的主观和客观质量。
（3）在终端的视频应用层，针对随机丢包差错，以立体图像为实验对象，利用像素点之间的视差相关性，提出了一种基于局部可信视差的误码掩盖算法。该算法选择丢失块周围一圈像素作为处理对象，对它们进行视差估计。首先，为了得到较准确的视差，设计了基准点偏置的匹配窗口，并根据需估计的像素点位于丢失块四周的不同方位，而采用不同的偏置中心窗口进行自适应权重的视差估计。然后根据视差左右一致性约束规则剔除不可信点，接着又采用“胜者为王”（WTA，Winner-Takes-All）准则进一步消除误估计视差，这种双消除策略使得估计出的丢失块视差具有较高的精度。最后根据估计的视差进行误码掩盖。实验结果表明，与其他算法相比，该算法能够在计算复杂度相当的情况下，能够显著地提高峰值信噪比（PSNR，Peak Signal to Noise Ratio），并取得较为优质的画面质量。
（4）在终端的视频应用层，利用块的视差相关性，提出了一种随机和连续丢包都适用的基于边界平滑性的立体图像误码掩盖算法。该算法选择丢失块四周相邻的上、下、左、右的区域作为处理区域，为这些区域采用块匹配策略估计视差。然后根据块间视差具有较高的相关性，利用上述得到的四个视差分别来掩盖丢失块，并计算掩盖后的边界平滑性，选择边界平滑性最好的结果作为初步掩盖结果。如果平滑性最好的结果仍大于阈值，则需要对丢失块进行内容自适应判断以得出其属于纹理块、方向块还是遮挡块，并根据判断的类型采用不同的算法修正初步掩盖结果。实验结果表明，相比于其他算法，该算法能够取得较高的PSNR和较好的主观效果，同时其复杂度又相对较小。
Wireless mesh networks (WMNs) are suitable for ubiquitous wide-band communicationservices because of the wide-area coverage, flexibility and low cost. It has become an importantnetworking mode of the next generation wireless communication system. However, itscharacteristics such as time-varying channel, multi-hop and node mobility will make the linksunstable, and consequently degrade the quality of service (QoS) for upper layers. Stereoscopicvideo which contains the depth information of the objects is suitable for3D natural scenes.Therefore it can provide more vivid visual experience for people. Nonetheless, we are facing thechallenges of dealing with large amounts of data and real-time transmission requirements inorder to preserve the stereoscopic video quality over mesh networks. This leads to the problem ofhow to guarantee the end-to-end video quality, and the research of this topic has importanttheoretical significance and practical application value.
This dissertation mainly focuses on key technologies of end-to-end QoS for stereoscopicvideo delivery over WMNs. Taking the characteristics of stereoscopic video into consideration,several methods are proposed at different layers from the perspective of transmission networkand terminal system. We set up the model of video distortion using the channel packet lossmodel, Markov model and M/M/1/K queue model in transmission network. We select thereasonable transmission rate at Medium Access Control (MAC) layer and select the proper routeat routing layer. At the application layer of the terminal system, the decoder implements the errorconcealment for corrosion images, making use of the correlation of disparity.
The contributions of the dissertation are summarized as following:
(1) In order to solve the bad fish problem in a shared single channel network, we propose arate selection mechanism at MAC layer which is suitable for video transmission. First, using thecross-layer technology, MAC layer gets the requirement of frame rate and the tolerance of packetloss rate for video from application layer. Second, the maximum allowed delay of each videopacket is estimated based on the frame rate requirement, which will be used to compute themaximum number of retransmissions for a video MAC frame. Finally, within the margin of thetolerant packet loss rate of application layer, we can adaptively select the highest possibletransmission rate for video MAC frames in terms of current channel quality and the maximumnumber of retransmissions. The lost packet will be dealt with using the algorithm of errorconcealment in the decoder. Experimental results show that, compared with the algorithm ofchanging contention window, the proposed method can reduce the delay and jitter of video service when the transmission rate should change during the Signal to Noise Ratio (SNR) ofvideo node declines, and make the throughputs of the transmission hosts degrade gracefully.Therefore, it increases the quality of reconstructed video to a certain extent and eliminates theperformance anomaly of network effectively.
(2) Aiming at the problem of several routes between the source node and the destinationnode, we use video distortion as the metric to predict the transmission distortion of a route, andselect the route with minimal distortion as the transmission route in terms of the functionalrelationship between the distortion and the network packet loss and delay. The proposedalgorithm considers the factors of channel error, route interference and network congestion. First,random uniform model and Gilbert-Elliott (GE) model are used to simulate the random and burstpacket loss of the channel. Second, Markov model is used to calculate the packet collisionprobability of MAC layer and the processing time, and M/M/1/K queue system is used to predictthe delay and the probability of packet loss. Finally, we calculate the distortion of the total routeand select the route with minimum distortion of the network. Experimental results show thatcompared with the Dynamic Source Routing (DSR) protocol, our algorithm can provide betterobjective and subjective quality of video.
(3) In order to eliminate the random errors for stereoscopic images in video applicationlayer of terminal system, we use the disparity correlation between pixels to propose an errorconcealment algorithm based on local reliable disparities. This algorithm uses the pixelssurrounding the lost block. First, in order to get accurate disparities, we design abase-point-biased window, and use different windows in terms of the position of estimated pixelto perform the adaptive-weight disparity matching. Second, reliable disparities of local area arefigured out according to the principles of disparity constancy of left-right consistency. And thenthe winner-takes-all strategy is adopted to remove the error disparities. These two strategiesmake the estimated disparity of the lost block better. Finally we use the estimated disparity torecover the lost block. Experimental results show that compared with other algorithms, theproposed method can significantly improve the Peak Signal to Noise Ratio (PSNR) value as wellas subjective quality with almost the same computational complexity.
(4) In order to solve the random and consecutive errors for stereoscopic images, we use thedisparity correlation of blocks to propose an error concealment algorithm based on thesmoothness of boundary. First, we select the up, down, left and right blocks around the lost block,and conduct the disparity matching for them. Second, we use these four disparities to conceal thelost block, and calculate the smoothness of the boundary respectively. Then we select the bestsmoothness as the probable concealment result. If the best smoothness is still beyond the threshold, we carry out the content adaptive judgment for the lost block. The lost block will beclassified into one of the three types which are the texture block, directional block, and occlusionblock. We use different algorithms to conceal the lost block according to its type. Experimentalresults show that the proposed method substantially outperforms other algorithms both visuallyand in terms of PSNR, and at the same time, it has a lower computational complexity.
引文
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